用于卫星轨道预测的速度耦合表示精化
Velocity-coupled Representation Refinement for Satellite Orbit Prediction
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中文总结 AI 辅助
该研究针对现有卫星轨道预测方法未利用位置与速度耦合关系的问题，提出OrbitNet方法，通过速度耦合表示精化和轨道片段建模，在Starlink域内及6个星座零样本评估中均优于对比模型。
中文摘要 AI 辅助
卫星轨道预测旨在从历史观测数据中预测未来轨道轨迹，对碰撞预警和安全的空间操作至关重要。随着时间序列预测技术的发展，基于学习的方法已成为卫星预测的有前景解决方案。在轨道动力学中，卫星状态通常由位置和速度描述：位置表征轨迹的几何形态，速度则反映其瞬时方向和变化率。然而，大多数现有方法主要关注位置序列内的时间依赖关系，很少利用位置与速度之间的固有耦合关系，而这种耦合对建模卫星运动至关重要。为此，我们提出OrbitNet，一种用于精确卫星轨道预测的速度感知表示学习方法。该方法通过利用卫星状态变量之间的关系，将传统的位置序列预测提升为位置-速度耦合表示学习范式。具体而言，我们开发了一种速度耦合表示精化策略，通过位置与速度之间的跨变量交互来增强位置表示。我们还引入了轨道片段建模，将历史轨迹划分为时间片段并执行片段级时间学习，以捕捉局部运动变化和远程演化模式。大量实验表明，在Starlink上的域内评估以及六个未见过的卫星星座上的零样本评估中，OrbitNet的性能优于大型时间序列基础模型和代表性的通用预测方法。我们期望这项工作能推动对面向轨迹时间序列预测的卫星感知表示学习的进一步探索。
英文摘要
Satellite orbit prediction, which aims to forecast future orbital trajectories from historical observations, is important for collision warning and safe space operations. With advances in time-series forecasting, learning-based methods have emerged as a promising solution for satellite prediction. In orbital dynamics, a satellite state is typically described by position and velocity, where position characterizes trajectory geometry and velocity reflects its instantaneous direction and rate of change. However, most existing methods mainly focus on temporal dependencies within position sequences while rarely exploiting the intrinsic coupling between position and velocity, which is essential for modeling satellite motion. To this end, we propose OrbitNet, a velocity-aware representation learning method for accurate satellite orbit prediction. It lifts conventional position-sequence forecasting to a position-velocity coupled representation learning paradigm by exploiting relationships among satellite state variables. Specifically, we develop a velocity-coupled representation refinement strategy to enhance positional representations through cross-variable interactions between position and velocity. We further introduce orbital segment modeling, which partitions historical trajectories into temporal segments and performs segment-level temporal learning to capture local motion variations and long-range evolution patterns. Extensive experiments show that OrbitNet outperforms large time-series foundation models and representative general forecasting methods under both in-domain evaluation on Starlink and zero-shot evaluation across six unseen satellite constellations. We expect this work to encourage further exploration of satellite-aware representation learning for trajectory time-series forecasting.
发表机构
- Xi’an Jiaotong University（西安交通大学）
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
